Overview

Dataset statistics

Number of variables38
Number of observations8973
Missing cells54775
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory304.0 B

Variable types

Numeric26
Categorical10
Boolean2

Warnings

razaoSocial has a high cardinality: 2759 distinct values High cardinality
nomeFantasia has a high cardinality: 2677 distinct values High cardinality
cnpjSemTraco has a high cardinality: 2829 distinct values High cardinality
primeiraCompra has a high cardinality: 1950 distinct values High cardinality
dataAprovadoEmComite has a high cardinality: 558 distinct values High cardinality
periodoBalanco has a high cardinality: 124 distinct values High cardinality
dataAprovadoNivelAnalista has a high cardinality: 7011 distinct values High cardinality
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
titulosEmAberto is highly correlated with valorAprovadoHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with titulosEmAbertoHigh correlation
ativoCirculante is highly correlated with passivoCirculante and 3 other fieldsHigh correlation
passivoCirculante is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
totalAtivo is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
endividamento is highly correlated with estoque and 3 other fieldsHigh correlation
duplicatasAReceber is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
estoque is highly correlated with endividamento and 3 other fieldsHigh correlation
faturamentoBruto is highly correlated with endividamento and 3 other fieldsHigh correlation
margemBruta is highly correlated with endividamento and 3 other fieldsHigh correlation
custos is highly correlated with endividamento and 3 other fieldsHigh correlation
capitalSocial is highly correlated with limiteEmpresaAnaliseCreditoHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with capitalSocialHigh correlation
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
prazoMedioRecebimentoVendas is highly correlated with titulosEmAbertoHigh correlation
titulosEmAberto is highly correlated with prazoMedioRecebimentoVendasHigh correlation
valorSolicitado is highly correlated with valorAprovado and 8 other fieldsHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with valorSolicitado and 10 other fieldsHigh correlation
ativoCirculante is highly correlated with valorSolicitado and 12 other fieldsHigh correlation
passivoCirculante is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
totalAtivo is highly correlated with valorSolicitado and 12 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with valorAprovado and 9 other fieldsHigh correlation
endividamento is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
duplicatasAReceber is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
estoque is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
faturamentoBruto is highly correlated with valorSolicitado and 10 other fieldsHigh correlation
margemBruta is highly correlated with valorSolicitado and 9 other fieldsHigh correlation
custos is highly correlated with valorSolicitado and 9 other fieldsHigh correlation
capitalSocial is highly correlated with ativoCirculante and 5 other fieldsHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with valorAprovado and 4 other fieldsHigh correlation
prazoMedioRecebimentoVendas is highly correlated with titulosEmAbertoHigh correlation
titulosEmAberto is highly correlated with prazoMedioRecebimentoVendasHigh correlation
valorSolicitado is highly correlated with valorAprovadoHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with valorSolicitado and 1 other fieldsHigh correlation
ativoCirculante is highly correlated with passivoCirculante and 7 other fieldsHigh correlation
passivoCirculante is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
totalAtivo is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with ativoCirculante and 4 other fieldsHigh correlation
duplicatasAReceber is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
estoque is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
faturamentoBruto is highly correlated with valorAprovado and 7 other fieldsHigh correlation
margemBruta is highly correlated with ativoCirculante and 6 other fieldsHigh correlation
custos is highly correlated with ativoCirculante and 6 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with anoFundacao and 7 other fieldsHigh correlation
anoFundacao is highly correlated with totalPatrimonioLiquido and 5 other fieldsHigh correlation
capitalSocial is highly correlated with totalPatrimonioLiquido and 4 other fieldsHigh correlation
status is highly correlated with diferencaPercentualRisco and 2 other fieldsHigh correlation
empresa_MeEppMei is highly correlated with diferencaPercentualRisco and 2 other fieldsHigh correlation
endividamento is highly correlated with capitalSocial and 5 other fieldsHigh correlation
diferencaPercentualRisco is highly correlated with status and 3 other fieldsHigh correlation
ativoCirculante is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
duplicatasAReceber is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with capitalSocial and 5 other fieldsHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
intervaloFundacao is highly correlated with definicaoRiscoHigh correlation
percentualRisco is highly correlated with status and 3 other fieldsHigh correlation
margemBruta is highly correlated with endividamento and 4 other fieldsHigh correlation
faturamentoBruto is highly correlated with totalPatrimonioLiquido and 11 other fieldsHigh correlation
definicaoRisco is highly correlated with status and 4 other fieldsHigh correlation
estoque is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
custos is highly correlated with endividamento and 8 other fieldsHigh correlation
totalAtivo is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
passivoCirculante is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
percentualProtestos has 1498 (16.7%) missing values Missing
primeiraCompra has 106 (1.2%) missing values Missing
valorAprovado has 1404 (15.6%) missing values Missing
dataAprovadoEmComite has 8415 (93.8%) missing values Missing
periodoBalanco has 4240 (47.3%) missing values Missing
ativoCirculante has 4240 (47.3%) missing values Missing
passivoCirculante has 4240 (47.3%) missing values Missing
totalAtivo has 4240 (47.3%) missing values Missing
totalPatrimonioLiquido has 4240 (47.3%) missing values Missing
endividamento has 4240 (47.3%) missing values Missing
duplicatasAReceber has 4240 (47.3%) missing values Missing
estoque has 4240 (47.3%) missing values Missing
faturamentoBruto has 750 (8.4%) missing values Missing
margemBruta has 750 (8.4%) missing values Missing
periodoDemonstrativoEmMeses has 750 (8.4%) missing values Missing
custos has 750 (8.4%) missing values Missing
anoFundacao has 745 (8.3%) missing values Missing
intervaloFundacao has 745 (8.3%) missing values Missing
capitalSocial has 745 (8.3%) missing values Missing
restricoes has 745 (8.3%) missing values Missing
empresa_MeEppMei has 745 (8.3%) missing values Missing
limiteEmpresaAnaliseCredito has 745 (8.3%) missing values Missing
dataAprovadoNivelAnalista has 1962 (21.9%) missing values Missing
percentualProtestos is highly skewed (γ1 = 44.01820396) Skewed
valorSolicitado is highly skewed (γ1 = 53.71239075) Skewed
ativoCirculante is highly skewed (γ1 = 52.13686631) Skewed
passivoCirculante is highly skewed (γ1 = 44.07491523) Skewed
totalAtivo is highly skewed (γ1 = 51.80559965) Skewed
totalPatrimonioLiquido is highly skewed (γ1 = 36.46554306) Skewed
duplicatasAReceber is highly skewed (γ1 = 64.88105035) Skewed
anoFundacao is highly skewed (γ1 = -34.09741697) Skewed
limiteEmpresaAnaliseCredito is highly skewed (γ1 = 51.13390401) Skewed
numero_solicitacao is uniformly distributed Uniform
dataAprovadoEmComite is uniformly distributed Uniform
dataAprovadoNivelAnalista is uniformly distributed Uniform
numero_solicitacao has unique values Unique
maiorAtraso has 1756 (19.6%) zeros Zeros
margemBrutaAcumulada has 1577 (17.6%) zeros Zeros
percentualProtestos has 7452 (83.0%) zeros Zeros
prazoMedioRecebimentoVendas has 5536 (61.7%) zeros Zeros
titulosEmAberto has 5042 (56.2%) zeros Zeros
percentualRisco has 761 (8.5%) zeros Zeros
dashboardCorrelacao has 5587 (62.3%) zeros Zeros
ativoCirculante has 554 (6.2%) zeros Zeros
passivoCirculante has 591 (6.6%) zeros Zeros
totalAtivo has 557 (6.2%) zeros Zeros
totalPatrimonioLiquido has 590 (6.6%) zeros Zeros
endividamento has 2381 (26.5%) zeros Zeros
duplicatasAReceber has 1029 (11.5%) zeros Zeros
estoque has 763 (8.5%) zeros Zeros
faturamentoBruto has 414 (4.6%) zeros Zeros
margemBruta has 4316 (48.1%) zeros Zeros
custos has 4429 (49.4%) zeros Zeros
capitalSocial has 129 (1.4%) zeros Zeros
scorePontualidade has 1556 (17.3%) zeros Zeros
limiteEmpresaAnaliseCredito has 528 (5.9%) zeros Zeros

Reproduction

Analysis started2021-08-21 12:43:33.797572
Analysis finished2021-08-21 12:44:56.396451
Duration1 minute and 22.6 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

numero_solicitacao
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct8973
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4550.042015
Minimum1
Maximum9045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:56.494446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile466.6
Q12316
median4559
Q36802
95-th percentile8596.4
Maximum9045
Range9044
Interquartile range (IQR)4486

Descriptive statistics

Standard deviation2603.485853
Coefficient of variation (CV)0.5721894094
Kurtosis-1.191509331
Mean4550.042015
Median Absolute Deviation (MAD)2243
Skewness-0.01192454409
Sum40827527
Variance6778138.588
MonotonicityStrictly increasing
2021-08-21T09:44:56.611445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
47111
 
< 0.1%
67501
 
< 0.1%
47031
 
< 0.1%
88011
 
< 0.1%
26601
 
< 0.1%
6131
 
< 0.1%
67581
 
< 0.1%
88091
 
< 0.1%
26521
 
< 0.1%
Other values (8963)8963
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
90451
< 0.1%
90441
< 0.1%
90431
< 0.1%
90421
< 0.1%
90411
< 0.1%
90401
< 0.1%
90391
< 0.1%
90381
< 0.1%
90371
< 0.1%
90361
< 0.1%

razaoSocial
Categorical

HIGH CARDINALITY

Distinct2759
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
Malcolm Bolton
 
25
Suzanne Smith
 
25
Douglas Taylor
 
21
Dr. Vanessa Bird
 
21
Dr. Jake Dale
 
20
Other values (2754)
8861 

Length

Max length28
Median length14
Mean length14.87529254
Min length7

Characters and Unicode

Total characters133476
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique907 ?
Unique (%)10.1%

Sample

1st rowJames Richardson-Patel
2nd rowDr. Geoffrey Walsh
3rd rowJoanna Hudson
4th rowGordon Jones-Hopkins
5th rowNigel Lee

Common Values

ValueCountFrequency (%)
Malcolm Bolton25
 
0.3%
Suzanne Smith25
 
0.3%
Douglas Taylor21
 
0.2%
Dr. Vanessa Bird21
 
0.2%
Dr. Jake Dale20
 
0.2%
Mr. Mohamed Howard20
 
0.2%
Kelly Fox19
 
0.2%
Brett Wheeler18
 
0.2%
Keith Jones18
 
0.2%
Mr. Danny Bradley17
 
0.2%
Other values (2749)8769
97.7%

Length

2021-08-21T09:44:56.855448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr1014
 
4.9%
mr702
 
3.4%
ms338
 
1.6%
mrs316
 
1.5%
miss285
 
1.4%
smith272
 
1.3%
jones226
 
1.1%
taylor142
 
0.7%
thomas109
 
0.5%
evans97
 
0.5%
Other values (1170)17100
83.0%

Most occurring characters

ValueCountFrequency (%)
11628
 
8.7%
e11409
 
8.5%
a10743
 
8.0%
r10690
 
8.0%
n9157
 
6.9%
o7542
 
5.7%
i7100
 
5.3%
l6877
 
5.2%
s6668
 
5.0%
t4218
 
3.2%
Other values (44)47444
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter96073
72.0%
Uppercase Letter22027
 
16.5%
Space Separator11628
 
8.7%
Other Punctuation2421
 
1.8%
Dash Punctuation1327
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11409
11.9%
a10743
11.2%
r10690
11.1%
n9157
9.5%
o7542
7.9%
i7100
7.4%
l6877
 
7.2%
s6668
 
6.9%
t4218
 
4.4%
h3945
 
4.1%
Other values (16)17724
18.4%
Uppercase Letter
ValueCountFrequency (%)
M3174
14.4%
D2147
 
9.7%
J1675
 
7.6%
S1645
 
7.5%
B1514
 
6.9%
C1319
 
6.0%
H1243
 
5.6%
R1141
 
5.2%
A1127
 
5.1%
W1114
 
5.1%
Other values (14)5928
26.9%
Other Punctuation
ValueCountFrequency (%)
.2370
97.9%
'51
 
2.1%
Space Separator
ValueCountFrequency (%)
11628
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118100
88.5%
Common15376
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11409
 
9.7%
a10743
 
9.1%
r10690
 
9.1%
n9157
 
7.8%
o7542
 
6.4%
i7100
 
6.0%
l6877
 
5.8%
s6668
 
5.6%
t4218
 
3.6%
h3945
 
3.3%
Other values (40)39751
33.7%
Common
ValueCountFrequency (%)
11628
75.6%
.2370
 
15.4%
-1327
 
8.6%
'51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII133476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11628
 
8.7%
e11409
 
8.5%
a10743
 
8.0%
r10690
 
8.0%
n9157
 
6.9%
o7542
 
5.7%
i7100
 
5.3%
l6877
 
5.2%
s6668
 
5.0%
t4218
 
3.2%
Other values (44)47444
35.5%

nomeFantasia
Categorical

HIGH CARDINALITY

Distinct2677
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
Nathan Jones
 
35
Marian Day
 
25
Dale Lowe
 
21
Anne Payne
 
21
Linda Bradley
 
20
Other values (2672)
8851 

Length

Max length28
Median length15
Mean length14.91251532
Min length8

Characters and Unicode

Total characters133810
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)9.4%

Sample

1st rowAlexandra Williams
2nd rowMr. Darren Arnold
3rd rowDr. David Rees
4th rowSara Reid-Robson
5th rowDr. Stanley Duncan

Common Values

ValueCountFrequency (%)
Nathan Jones35
 
0.4%
Marian Day25
 
0.3%
Dale Lowe21
 
0.2%
Anne Payne21
 
0.2%
Linda Bradley20
 
0.2%
Mr. Ricky Williams20
 
0.2%
Lorraine Hughes19
 
0.2%
Janet Owen19
 
0.2%
Leon Smith18
 
0.2%
Mrs. Beverley Khan17
 
0.2%
Other values (2667)8758
97.6%

Length

2021-08-21T09:44:57.090479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr1011
 
4.9%
mr637
 
3.1%
mrs333
 
1.6%
ms311
 
1.5%
miss295
 
1.4%
jones220
 
1.1%
smith207
 
1.0%
williams137
 
0.7%
thomas95
 
0.5%
taylor94
 
0.5%
Other values (1168)17193
83.7%

Most occurring characters

ValueCountFrequency (%)
11560
 
8.6%
e11496
 
8.6%
a10694
 
8.0%
r10348
 
7.7%
n9857
 
7.4%
i7391
 
5.5%
o7370
 
5.5%
l7281
 
5.4%
s6528
 
4.9%
t4251
 
3.2%
Other values (44)47034
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter96678
72.3%
Uppercase Letter21928
 
16.4%
Space Separator11560
 
8.6%
Other Punctuation2358
 
1.8%
Dash Punctuation1286
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11496
11.9%
a10694
11.1%
r10348
10.7%
n9857
10.2%
i7391
7.6%
o7370
7.6%
l7281
7.5%
s6528
 
6.8%
t4251
 
4.4%
h3746
 
3.9%
Other values (16)17716
18.3%
Uppercase Letter
ValueCountFrequency (%)
M2933
13.4%
D2108
 
9.6%
J1651
 
7.5%
S1613
 
7.4%
B1468
 
6.7%
C1387
 
6.3%
H1219
 
5.6%
A1191
 
5.4%
W1046
 
4.8%
R1022
 
4.7%
Other values (14)6290
28.7%
Other Punctuation
ValueCountFrequency (%)
.2292
97.2%
'66
 
2.8%
Space Separator
ValueCountFrequency (%)
11560
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118606
88.6%
Common15204
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11496
 
9.7%
a10694
 
9.0%
r10348
 
8.7%
n9857
 
8.3%
i7391
 
6.2%
o7370
 
6.2%
l7281
 
6.1%
s6528
 
5.5%
t4251
 
3.6%
h3746
 
3.2%
Other values (40)39644
33.4%
Common
ValueCountFrequency (%)
11560
76.0%
.2292
 
15.1%
-1286
 
8.5%
'66
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII133810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11560
 
8.6%
e11496
 
8.6%
a10694
 
8.0%
r10348
 
7.7%
n9857
 
7.4%
i7391
 
5.5%
o7370
 
5.5%
l7281
 
5.4%
s6528
 
4.9%
t4251
 
3.2%
Other values (44)47034
35.1%

cnpjSemTraco
Categorical

HIGH CARDINALITY

Distinct2829
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
AVAO63044598911311
 
25
JXCH36268697453955
 
21
GTPO06511661214973
 
21
VVSW90409251685348
 
20
SPID07567212738639
 
20
Other values (2824)
8866 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters161514
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique940 ?
Unique (%)10.5%

Sample

1st rowKEBE17609492220843
2nd rowJRBK88908250677300
3rd rowGCVQ28531614261293
4th rowKJND32266018316396
5th rowCGQN15826802440348

Common Values

ValueCountFrequency (%)
AVAO6304459891131125
 
0.3%
JXCH3626869745395521
 
0.2%
GTPO0651166121497321
 
0.2%
VVSW9040925168534820
 
0.2%
SPID0756721273863920
 
0.2%
DNLY3538074806702819
 
0.2%
DQHR2899319099033117
 
0.2%
AMVK7095078513948116
 
0.2%
DSDP4291529621354116
 
0.2%
YFOX8908108327545216
 
0.2%
Other values (2819)8782
97.9%

Length

2021-08-21T09:44:57.321445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
avao6304459891131125
 
0.3%
gtpo0651166121497321
 
0.2%
jxch3626869745395521
 
0.2%
spid0756721273863920
 
0.2%
vvsw9040925168534820
 
0.2%
dnly3538074806702819
 
0.2%
dqhr2899319099033117
 
0.2%
dsdp4291529621354116
 
0.2%
brjp3745343715459216
 
0.2%
amvk7095078513948116
 
0.2%
Other values (2819)8782
97.9%

Most occurring characters

ValueCountFrequency (%)
113247
 
8.2%
512691
 
7.9%
412638
 
7.8%
612620
 
7.8%
212538
 
7.8%
712530
 
7.8%
912508
 
7.7%
312448
 
7.7%
812275
 
7.6%
012127
 
7.5%
Other values (26)35892
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125622
77.8%
Uppercase Letter35892
 
22.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1556
 
4.3%
V1549
 
4.3%
Y1512
 
4.2%
O1487
 
4.1%
A1479
 
4.1%
X1471
 
4.1%
N1446
 
4.0%
Q1439
 
4.0%
J1436
 
4.0%
B1425
 
4.0%
Other values (16)21092
58.8%
Decimal Number
ValueCountFrequency (%)
113247
10.5%
512691
10.1%
412638
10.1%
612620
10.0%
212538
10.0%
712530
10.0%
912508
10.0%
312448
9.9%
812275
9.8%
012127
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common125622
77.8%
Latin35892
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1556
 
4.3%
V1549
 
4.3%
Y1512
 
4.2%
O1487
 
4.1%
A1479
 
4.1%
X1471
 
4.1%
N1446
 
4.0%
Q1439
 
4.0%
J1436
 
4.0%
B1425
 
4.0%
Other values (16)21092
58.8%
Common
ValueCountFrequency (%)
113247
10.5%
512691
10.1%
412638
10.1%
612620
10.0%
212538
10.0%
712530
10.0%
912508
10.0%
312448
9.9%
812275
9.8%
012127
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII161514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113247
 
8.2%
512691
 
7.9%
412638
 
7.8%
612620
 
7.8%
212538
 
7.8%
712530
 
7.8%
912508
 
7.7%
312448
 
7.7%
812275
 
7.6%
012127
 
7.5%
Other values (26)35892
22.2%

maiorAtraso
Real number (ℝ≥0)

ZEROS

Distinct175
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.64259445
Minimum0
Maximum1265
Zeros1756
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:57.417480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q322
95-th percentile95
Maximum1265
Range1265
Interquartile range (IQR)19

Descriptive statistics

Standard deviation66.18079272
Coefficient of variation (CV)2.685626015
Kurtosis93.60376135
Mean24.64259445
Median Absolute Deviation (MAD)6
Skewness8.38624533
Sum221118
Variance4379.897325
MonotonicityNot monotonic
2021-08-21T09:44:57.550449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01756
19.6%
31024
 
11.4%
4732
 
8.2%
5448
 
5.0%
6381
 
4.2%
2276
 
3.1%
7218
 
2.4%
8207
 
2.3%
18190
 
2.1%
9178
 
2.0%
Other values (165)3563
39.7%
ValueCountFrequency (%)
01756
19.6%
112
 
0.1%
2276
 
3.1%
31024
11.4%
4732
8.2%
5448
 
5.0%
6381
 
4.2%
7218
 
2.4%
8207
 
2.3%
9178
 
2.0%
ValueCountFrequency (%)
12651
 
< 0.1%
9776
0.1%
8071
 
< 0.1%
7945
0.1%
77910
0.1%
7404
 
< 0.1%
6905
0.1%
6293
 
< 0.1%
6142
 
< 0.1%
5893
 
< 0.1%

margemBrutaAcumulada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2145
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3621762216
Minimum0
Maximum1
Zeros1577
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:57.672447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2813948662
median0.4028954164
Q30.5078601917
95-th percentile0.624934082
Maximum1
Range1
Interquartile range (IQR)0.2264653255

Descriptive statistics

Standard deviation0.2014554104
Coefficient of variation (CV)0.5562358829
Kurtosis-0.4976979867
Mean0.3621762216
Median Absolute Deviation (MAD)0.110761786
Skewness-0.6402790022
Sum3249.807237
Variance0.04058428238
MonotonicityNot monotonic
2021-08-21T09:44:57.801480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01577
 
17.6%
0.565045773925
 
0.3%
0.374572665621
 
0.2%
0.371676033421
 
0.2%
0.470681428120
 
0.2%
0.412316285720
 
0.2%
0.478865639519
 
0.2%
0.443677127417
 
0.2%
0.653368148116
 
0.2%
0.444543829316
 
0.2%
Other values (2135)7221
80.5%
ValueCountFrequency (%)
01577
17.6%
1.20858 × 10-55
 
0.1%
0.00400164313
 
< 0.1%
0.01186610291
 
< 0.1%
0.01986093912
 
< 0.1%
0.02167777184
 
< 0.1%
0.02798962962
 
< 0.1%
0.04088205716
 
0.1%
0.04862851573
 
< 0.1%
0.05464732552
 
< 0.1%
ValueCountFrequency (%)
16
0.1%
0.93758237752
 
< 0.1%
0.89466822431
 
< 0.1%
0.83487434781
 
< 0.1%
0.82575231484
< 0.1%
0.80635524621
 
< 0.1%
0.8002998853
 
< 0.1%
0.7758119531
 
< 0.1%
0.76840772238
0.1%
0.75785694443
 
< 0.1%

percentualProtestos
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct10
Distinct (%)0.1%
Missing1498
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.01926042325
Minimum0
Maximum36.98372833
Zeros7452
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:57.900482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36.98372833
Range36.98372833
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5935788833
Coefficient of variation (CV)30.81857941
Kurtosis2307.399511
Mean0.01926042325
Median Absolute Deviation (MAD)0
Skewness44.01820396
Sum143.9716638
Variance0.3523358906
MonotonicityNot monotonic
2021-08-21T09:44:57.979479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
07452
83.0%
15.298109555
 
0.1%
0.590745863
 
< 0.1%
3.035994823
 
< 0.1%
0.955607843
 
< 0.1%
1.70257633
 
< 0.1%
0.495062323
 
< 0.1%
6.769564181
 
< 0.1%
3.387862091
 
< 0.1%
36.983728331
 
< 0.1%
(Missing)1498
 
16.7%
ValueCountFrequency (%)
07452
83.0%
0.495062323
 
< 0.1%
0.590745863
 
< 0.1%
0.955607843
 
< 0.1%
1.70257633
 
< 0.1%
3.035994823
 
< 0.1%
3.387862091
 
< 0.1%
6.769564181
 
< 0.1%
15.298109555
 
0.1%
36.983728331
 
< 0.1%
ValueCountFrequency (%)
36.983728331
 
< 0.1%
15.298109555
 
0.1%
6.769564181
 
< 0.1%
3.387862091
 
< 0.1%
3.035994823
 
< 0.1%
1.70257633
 
< 0.1%
0.955607843
 
< 0.1%
0.590745863
 
< 0.1%
0.495062323
 
< 0.1%
07452
83.0%

primeiraCompra
Categorical

HIGH CARDINALITY
MISSING

Distinct1950
Distinct (%)22.0%
Missing106
Missing (%)1.2%
Memory size70.2 KiB
2012-12-21T00:00:00
 
35
2011-08-02T00:00:00
 
28
2018-09-28T00:00:00
 
25
2019-12-17T00:00:00
 
25
2013-01-31T00:00:00
 
25
Other values (1945)
8729 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters168473
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)5.5%

Sample

1st row2015-12-10T00:00:00
2nd row2019-06-12T17:28:31
3rd row2019-11-27T00:00:00
4th row2017-02-13T17:20:27
5th row2010-07-13T00:00:00

Common Values

ValueCountFrequency (%)
2012-12-21T00:00:0035
 
0.4%
2011-08-02T00:00:0028
 
0.3%
2018-09-28T00:00:0025
 
0.3%
2019-12-17T00:00:0025
 
0.3%
2013-01-31T00:00:0025
 
0.3%
2017-09-04T00:00:0024
 
0.3%
2018-10-17T00:00:0024
 
0.3%
2020-05-25T00:00:0024
 
0.3%
2019-07-29T00:00:0023
 
0.3%
2014-02-21T00:00:0021
 
0.2%
Other values (1940)8613
96.0%
(Missing)106
 
1.2%

Length

2021-08-21T09:44:58.213446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-12-21t00:00:0035
 
0.4%
2011-08-02t00:00:0028
 
0.3%
2018-09-28t00:00:0025
 
0.3%
2019-12-17t00:00:0025
 
0.3%
2013-01-31t00:00:0025
 
0.3%
2020-05-25t00:00:0024
 
0.3%
2018-10-17t00:00:0024
 
0.3%
2017-09-04t00:00:0024
 
0.3%
2019-07-29t00:00:0023
 
0.3%
2020-06-22t00:00:0021
 
0.2%
Other values (1940)8613
97.1%

Most occurring characters

ValueCountFrequency (%)
067542
40.1%
-17734
 
10.5%
:17734
 
10.5%
116732
 
9.9%
216327
 
9.7%
T8867
 
5.3%
73766
 
2.2%
33681
 
2.2%
83570
 
2.1%
93540
 
2.1%
Other values (3)8980
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124138
73.7%
Dash Punctuation17734
 
10.5%
Other Punctuation17734
 
10.5%
Uppercase Letter8867
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
067542
54.4%
116732
 
13.5%
216327
 
13.2%
73766
 
3.0%
33681
 
3.0%
83570
 
2.9%
93540
 
2.9%
63382
 
2.7%
52819
 
2.3%
42779
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-17734
100.0%
Uppercase Letter
ValueCountFrequency (%)
T8867
100.0%
Other Punctuation
ValueCountFrequency (%)
:17734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common159606
94.7%
Latin8867
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
067542
42.3%
-17734
 
11.1%
:17734
 
11.1%
116732
 
10.5%
216327
 
10.2%
73766
 
2.4%
33681
 
2.3%
83570
 
2.2%
93540
 
2.2%
63382
 
2.1%
Other values (2)5598
 
3.5%
Latin
ValueCountFrequency (%)
T8867
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII168473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
067542
40.1%
-17734
 
10.5%
:17734
 
10.5%
116732
 
9.9%
216327
 
9.7%
T8867
 
5.3%
73766
 
2.2%
33681
 
2.2%
83570
 
2.1%
93540
 
2.1%
Other values (3)8980
 
5.3%

prazoMedioRecebimentoVendas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct180
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.08302686
Minimum0
Maximum1605
Zeros5536
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:58.320447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile97
Maximum1605
Range1605
Interquartile range (IQR)30

Descriptive statistics

Standard deviation68.17764909
Coefficient of variation (CV)2.953583579
Kurtosis218.0347581
Mean23.08302686
Median Absolute Deviation (MAD)0
Skewness11.67843106
Sum207124
Variance4648.191835
MonotonicityNot monotonic
2021-08-21T09:44:58.445482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05536
61.7%
4284
 
0.9%
3480
 
0.9%
2679
 
0.9%
5276
 
0.8%
3275
 
0.8%
3774
 
0.8%
3972
 
0.8%
2771
 
0.8%
4168
 
0.8%
Other values (170)2758
30.7%
ValueCountFrequency (%)
05536
61.7%
121
 
0.2%
233
 
0.4%
320
 
0.2%
439
 
0.4%
522
 
0.2%
643
 
0.5%
79
 
0.1%
865
 
0.7%
931
 
0.3%
ValueCountFrequency (%)
16056
 
0.1%
72316
0.2%
50717
0.2%
3574
 
< 0.1%
3552
 
< 0.1%
3452
 
< 0.1%
3443
 
< 0.1%
3416
 
0.1%
3381
 
< 0.1%
3281
 
< 0.1%

titulosEmAberto
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct761
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64871.00633
Minimum0
Maximum3938589.7
Zeros5042
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:58.575482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317250
95-th percentile365200.78
Maximum3938589.7
Range3938589.7
Interquartile range (IQR)17250

Descriptive statistics

Standard deviation248285.1534
Coefficient of variation (CV)3.827367069
Kurtosis74.82464508
Mean64871.00633
Median Absolute Deviation (MAD)0
Skewness7.471972792
Sum582087539.8
Variance6.164551738 × 1010
MonotonicityNot monotonic
2021-08-21T09:44:58.698477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05042
56.2%
118195.8725
 
0.3%
17648021
 
0.2%
90290.921
 
0.2%
2079720
 
0.2%
529387.0420
 
0.2%
11337019
 
0.2%
5760018
 
0.2%
154941.0917
 
0.2%
183947.8416
 
0.2%
Other values (751)3754
41.8%
ValueCountFrequency (%)
05042
56.2%
12.054
 
< 0.1%
3158
 
0.1%
533.346
 
0.1%
6604
 
< 0.1%
693.336
 
0.1%
711.11
 
< 0.1%
7764
 
< 0.1%
8405
 
0.1%
894.41
 
< 0.1%
ValueCountFrequency (%)
3938589.75
 
0.1%
283665616
0.2%
2140954.393
 
< 0.1%
1985132.9113
0.1%
1913477.538
0.1%
1693918.9910
0.1%
1491736.3714
0.2%
13744501
 
< 0.1%
1276462.668
0.1%
1180370.6110
0.1%

valorSolicitado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct363
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749243.5877
Minimum100
Maximum1500000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:58.826480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile10000
Q125000
median50000
Q3120000
95-th percentile1100000
Maximum1500000000
Range1499999900
Interquartile range (IQR)95000

Descriptive statistics

Standard deviation22618752.86
Coefficient of variation (CV)30.18878404
Kurtosis3147.965692
Mean749243.5877
Median Absolute Deviation (MAD)30000
Skewness53.71239075
Sum6722962712
Variance5.11607981 × 1014
MonotonicityNot monotonic
2021-08-21T09:44:58.945526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001077
 
12.0%
20000890
 
9.9%
30000796
 
8.9%
15000500
 
5.6%
10000499
 
5.6%
40000491
 
5.5%
100000452
 
5.0%
25000244
 
2.7%
150000220
 
2.5%
60000201
 
2.2%
Other values (353)3603
40.2%
ValueCountFrequency (%)
1003
 
< 0.1%
1501
 
< 0.1%
4001
 
< 0.1%
16001
 
< 0.1%
25001
 
< 0.1%
29001
 
< 0.1%
30008
 
0.1%
40003
 
< 0.1%
5000136
1.5%
52001
 
< 0.1%
ValueCountFrequency (%)
15000000001
< 0.1%
12000000001
< 0.1%
6000000002
< 0.1%
3500000001
< 0.1%
1500000001
< 0.1%
1287000002
< 0.1%
510000001
< 0.1%
270000001
< 0.1%
107000001
< 0.1%
93000002
< 0.1%

status
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
AprovadoAnalista
7011 
ReprovadoAnalista
 
590
AprovadoComite
 
558
DocumentacaoReprovada
 
504
EmAnaliseDocumentacao
 
289
Other values (2)
 
21

Length

Max length21
Median length16
Mean length16.38136632
Min length14

Characters and Unicode

Total characters146990
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAprovadoAnalista
2nd rowDocumentacaoReprovada
3rd rowAprovadoAnalista
4th rowAprovadoAnalista
5th rowAprovadoAnalista

Common Values

ValueCountFrequency (%)
AprovadoAnalista7011
78.1%
ReprovadoAnalista590
 
6.6%
AprovadoComite558
 
6.2%
DocumentacaoReprovada504
 
5.6%
EmAnaliseDocumentacao289
 
3.2%
ReprovadoComite20
 
0.2%
AguardandoAprovacao1
 
< 0.1%

Length

2021-08-21T09:44:59.151525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T09:44:59.221522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
aprovadoanalista7011
78.1%
reprovadoanalista590
 
6.6%
aprovadocomite558
 
6.2%
documentacaoreprovada504
 
5.6%
emanalisedocumentacao289
 
3.2%
reprovadocomite20
 
0.2%
aguardandoaprovacao1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a26268
17.9%
o19029
12.9%
A15461
10.5%
t8972
 
6.1%
r8685
 
5.9%
d8685
 
5.9%
p8684
 
5.9%
v8684
 
5.9%
n8684
 
5.9%
i8468
 
5.8%
Other values (11)25370
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter128755
87.6%
Uppercase Letter18235
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a26268
20.4%
o19029
14.8%
t8972
 
7.0%
r8685
 
6.7%
d8685
 
6.7%
p8684
 
6.7%
v8684
 
6.7%
n8684
 
6.7%
i8468
 
6.6%
l7890
 
6.1%
Other values (6)14706
11.4%
Uppercase Letter
ValueCountFrequency (%)
A15461
84.8%
R1114
 
6.1%
D793
 
4.3%
C578
 
3.2%
E289
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin146990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a26268
17.9%
o19029
12.9%
A15461
10.5%
t8972
 
6.1%
r8685
 
5.9%
d8685
 
5.9%
p8684
 
5.9%
v8684
 
5.9%
n8684
 
5.9%
i8468
 
5.8%
Other values (11)25370
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII146990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a26268
17.9%
o19029
12.9%
A15461
10.5%
t8972
 
6.1%
r8685
 
5.9%
d8685
 
5.9%
p8684
 
5.9%
v8684
 
5.9%
n8684
 
5.9%
i8468
 
5.8%
Other values (11)25370
17.3%

definicaoRisco
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
De 11 a 30 % - Baixo
4486 
De 31 a 50 % - Médio
2509 
De 0 a 10 % - Muito Baixo
1590 
De 51 a 80 % - Alto
 
388

Length

Max length25
Median length20
Mean length20.84275047
Min length19

Characters and Unicode

Total characters187022
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDe 11 a 30 % - Baixo
2nd rowDe 0 a 10 % - Muito Baixo
3rd rowDe 11 a 30 % - Baixo
4th rowDe 51 a 80 % - Alto
5th rowDe 11 a 30 % - Baixo

Common Values

ValueCountFrequency (%)
De 11 a 30 % - Baixo4486
50.0%
De 31 a 50 % - Médio2509
28.0%
De 0 a 10 % - Muito Baixo1590
 
17.7%
De 51 a 80 % - Alto388
 
4.3%

Length

2021-08-21T09:44:59.407577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T09:44:59.472524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
17946
27.9%
a8973
13.9%
de8973
13.9%
baixo6076
 
9.4%
304486
 
7.0%
114486
 
7.0%
312509
 
3.9%
502509
 
3.9%
médio2509
 
3.9%
101590
 
2.5%
Other values (5)4344
 
6.7%

Most occurring characters

ValueCountFrequency (%)
55428
29.6%
a15049
 
8.0%
113459
 
7.2%
010563
 
5.6%
o10563
 
5.6%
i10175
 
5.4%
D8973
 
4.8%
e8973
 
4.8%
%8973
 
4.8%
-8973
 
4.8%
Other values (12)35893
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59810
32.0%
Space Separator55428
29.6%
Decimal Number34302
18.3%
Uppercase Letter19536
 
10.4%
Other Punctuation8973
 
4.8%
Dash Punctuation8973
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a15049
25.2%
o10563
17.7%
i10175
17.0%
e8973
15.0%
x6076
10.2%
é2509
 
4.2%
d2509
 
4.2%
t1978
 
3.3%
u1590
 
2.7%
l388
 
0.6%
Decimal Number
ValueCountFrequency (%)
113459
39.2%
010563
30.8%
36995
20.4%
52897
 
8.4%
8388
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
D8973
45.9%
B6076
31.1%
M4099
21.0%
A388
 
2.0%
Space Separator
ValueCountFrequency (%)
55428
100.0%
Other Punctuation
ValueCountFrequency (%)
%8973
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common107676
57.6%
Latin79346
42.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a15049
19.0%
o10563
13.3%
i10175
12.8%
D8973
11.3%
e8973
11.3%
B6076
7.7%
x6076
7.7%
M4099
 
5.2%
é2509
 
3.2%
d2509
 
3.2%
Other values (4)4344
 
5.5%
Common
ValueCountFrequency (%)
55428
51.5%
113459
 
12.5%
010563
 
9.8%
%8973
 
8.3%
-8973
 
8.3%
36995
 
6.5%
52897
 
2.7%
8388
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII184513
98.7%
Latin 1 Sup2509
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55428
30.0%
a15049
 
8.2%
113459
 
7.3%
010563
 
5.7%
o10563
 
5.7%
i10175
 
5.5%
D8973
 
4.9%
e8973
 
4.9%
%8973
 
4.9%
-8973
 
4.9%
Other values (11)33384
18.1%
Latin 1 Sup
ValueCountFrequency (%)
é2509
100.0%

diferencaPercentualRisco
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7503207436
Minimum0.2075471698
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:59.566522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.2075471698
5-th percentile0.5
Q10.6428571429
median0.75
Q30.8571428571
95-th percentile1
Maximum1
Range0.7924528302
Interquartile range (IQR)0.2142857143

Descriptive statistics

Standard deviation0.1460577753
Coefficient of variation (CV)0.1946604523
Kurtosis-0.3849968528
Mean0.7503207436
Median Absolute Deviation (MAD)0.1071428571
Skewness-0.2200139062
Sum6732.628032
Variance0.02133287374
MonotonicityNot monotonic
2021-08-21T09:44:59.690489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75778
 
8.7%
1761
 
8.5%
0.8571428571735
 
8.2%
0.8035714286590
 
6.6%
0.6428571429469
 
5.2%
0.6964285714426
 
4.7%
0.9107142857389
 
4.3%
0.6607142857236
 
2.6%
0.5357142857215
 
2.4%
0.7142857143210
 
2.3%
Other values (69)4164
46.4%
ValueCountFrequency (%)
0.20754716982
 
< 0.1%
0.26415094341
 
< 0.1%
0.28301886791
 
< 0.1%
0.30188679253
 
< 0.1%
0.3207547174
 
< 0.1%
0.33928571438
0.1%
0.33962264159
0.1%
0.35714285713
 
< 0.1%
0.3584905664
 
< 0.1%
0.37512
0.1%
ValueCountFrequency (%)
1761
8.5%
0.98214285713
 
< 0.1%
0.9642857143164
 
1.8%
0.962264150913
 
0.1%
0.946428571467
 
0.7%
0.943396226413
 
0.1%
0.928571428613
 
0.1%
0.92452830191
 
< 0.1%
0.9107142857389
4.3%
0.905660377422
 
0.2%

percentualRisco
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2496792564
Minimum0
Maximum0.7924528302
Zeros761
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:44:59.828524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1428571429
median0.25
Q30.3571428571
95-th percentile0.5
Maximum0.7924528302
Range0.7924528302
Interquartile range (IQR)0.2142857143

Descriptive statistics

Standard deviation0.1460577753
Coefficient of variation (CV)0.5849816178
Kurtosis-0.3849968528
Mean0.2496792564
Median Absolute Deviation (MAD)0.1071428571
Skewness0.2200139062
Sum2240.371968
Variance0.02133287374
MonotonicityNot monotonic
2021-08-21T09:44:59.953489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
 
8.5%
0.1428571429735
 
8.2%
0.25677
 
7.5%
0.1964285714590
 
6.6%
0.3571428571469
 
5.2%
0.3035714286426
 
4.7%
0.08928571429389
 
4.3%
0.3392857143236
 
2.6%
0.4642857143215
 
2.4%
0.2857142857210
 
2.3%
Other values (71)4265
47.5%
ValueCountFrequency (%)
0761
8.5%
0.017857142863
 
< 0.1%
0.03571428571164
 
1.8%
0.0377358490613
 
0.1%
0.0535714285767
 
0.7%
0.0566037735813
 
0.1%
0.0714285714313
 
0.1%
0.075471698111
 
< 0.1%
0.08928571429389
4.3%
0.0943396226422
 
0.2%
ValueCountFrequency (%)
0.79245283022
 
< 0.1%
0.73584905661
 
< 0.1%
0.71698113211
 
< 0.1%
0.69811320753
 
< 0.1%
0.6792452834
< 0.1%
0.66071428578
0.1%
0.66037735859
0.1%
0.64285714293
 
< 0.1%
0.6415094344
< 0.1%
0.6251
 
< 0.1%

dashboardCorrelacao
Real number (ℝ)

ZEROS

Distinct701
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04723573581
Minimum-0.9999901219
Maximum0.9999901219
Zeros5587
Zeros (%)62.3%
Negative1406
Negative (%)15.7%
Memory size70.2 KiB
2021-08-21T09:45:00.082491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.9999901219
5-th percentile-0.8660254038
Q10
median0
Q30
95-th percentile0.8660254038
Maximum0.9999901219
Range1.999980244
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4724756383
Coefficient of variation (CV)10.00250404
Kurtosis0.4064589135
Mean0.04723573581
Median Absolute Deviation (MAD)0
Skewness0.02704648245
Sum423.8462574
Variance0.2232332288
MonotonicityNot monotonic
2021-08-21T09:45:00.204492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05587
62.3%
0.8660254038826
 
9.2%
-0.8660254038347
 
3.9%
0.866025403879
 
0.9%
-0.866025403867
 
0.7%
3 × 10-1622
 
0.2%
-2 × 10-1617
 
0.2%
-1.1 × 10-1515
 
0.2%
-0.866025403814
 
0.2%
0.94412020214
 
0.2%
Other values (691)1985
 
22.1%
ValueCountFrequency (%)
-0.99999012195
0.1%
-0.99998540197
0.1%
-0.99998193582
 
< 0.1%
-0.99986814691
 
< 0.1%
-0.99981264671
 
< 0.1%
-0.99980828341
 
< 0.1%
-0.99973121381
 
< 0.1%
-0.99947900271
 
< 0.1%
-0.99944970431
 
< 0.1%
-0.99943194451
 
< 0.1%
ValueCountFrequency (%)
0.99999012191
 
< 0.1%
0.99992412266
0.1%
0.99992297543
< 0.1%
0.99983760923
< 0.1%
0.99980828344
< 0.1%
0.99976283721
 
< 0.1%
0.99968277231
 
< 0.1%
0.99966707113
< 0.1%
0.99953320334
< 0.1%
0.99947900271
 
< 0.1%

valorAprovado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct326
Distinct (%)4.3%
Missing1404
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean189792.577
Minimum0
Maximum10700000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:00.337489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5000
Q115100
median35000
Q3100000
95-th percentile1000000
Maximum10700000
Range10700000
Interquartile range (IQR)84900

Descriptive statistics

Standard deviation543518.5782
Coefficient of variation (CV)2.863750453
Kurtosis70.11249456
Mean189792.577
Median Absolute Deviation (MAD)25000
Skewness6.945329581
Sum1436540015
Variance2.954124448 × 1011
MonotonicityNot monotonic
2021-08-21T09:45:00.462490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000735
 
8.2%
10000730
 
8.1%
15000604
 
6.7%
30000582
 
6.5%
50000533
 
5.9%
40000375
 
4.2%
5000357
 
4.0%
25000295
 
3.3%
100000236
 
2.6%
35000202
 
2.3%
Other values (316)2920
32.5%
(Missing)1404
15.6%
ValueCountFrequency (%)
04
< 0.1%
11
 
< 0.1%
102
 
< 0.1%
1201
 
< 0.1%
16001
 
< 0.1%
20001
 
< 0.1%
25001
 
< 0.1%
29001
 
< 0.1%
30008
0.1%
35001
 
< 0.1%
ValueCountFrequency (%)
107000001
 
< 0.1%
92000001
 
< 0.1%
69000002
< 0.1%
65500001
 
< 0.1%
65000003
< 0.1%
62000001
 
< 0.1%
60000002
< 0.1%
59000001
 
< 0.1%
55000001
 
< 0.1%
51600002
< 0.1%

dataAprovadoEmComite
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct558
Distinct (%)100.0%
Missing8415
Missing (%)93.8%
Memory size70.2 KiB
2020-03-06T19:23:22
 
1
2020-05-21T16:18:14
 
1
2020-03-13T17:40:48
 
1
2020-02-12T20:26:50
 
1
2020-02-12T20:14:14
 
1
Other values (553)
553 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters10602
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique558 ?
Unique (%)100.0%

Sample

1st row2020-02-05T19:14:37
2nd row2020-02-07T18:57:37
3rd row2020-02-21T12:42:19
4th row2020-02-12T20:24:50
5th row2020-02-12T20:45:57

Common Values

ValueCountFrequency (%)
2020-03-06T19:23:221
 
< 0.1%
2020-05-21T16:18:141
 
< 0.1%
2020-03-13T17:40:481
 
< 0.1%
2020-02-12T20:26:501
 
< 0.1%
2020-02-12T20:14:141
 
< 0.1%
2020-03-06T19:57:141
 
< 0.1%
2020-03-13T14:53:021
 
< 0.1%
2020-03-13T17:37:431
 
< 0.1%
2020-03-06T12:04:591
 
< 0.1%
2020-03-06T20:01:241
 
< 0.1%
Other values (548)548
 
6.1%
(Missing)8415
93.8%

Length

2021-08-21T09:45:00.692491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-03-18t20:58:131
 
0.2%
2021-01-08t13:54:321
 
0.2%
2020-02-12t20:53:201
 
0.2%
2020-03-20t18:25:221
 
0.2%
2020-06-05t14:59:151
 
0.2%
2020-02-12t20:24:501
 
0.2%
2020-02-13t19:09:011
 
0.2%
2020-02-12t18:20:491
 
0.2%
2020-02-13t17:30:551
 
0.2%
2020-08-19t19:37:461
 
0.2%
Other values (548)548
98.2%

Most occurring characters

ValueCountFrequency (%)
02193
20.7%
21909
18.0%
11155
10.9%
-1116
10.5%
:1116
10.5%
3649
 
6.1%
T558
 
5.3%
4398
 
3.8%
5395
 
3.7%
8342
 
3.2%
Other values (3)771
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7812
73.7%
Dash Punctuation1116
 
10.5%
Other Punctuation1116
 
10.5%
Uppercase Letter558
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02193
28.1%
21909
24.4%
11155
14.8%
3649
 
8.3%
4398
 
5.1%
5395
 
5.1%
8342
 
4.4%
9287
 
3.7%
7285
 
3.6%
6199
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
-1116
100.0%
Uppercase Letter
ValueCountFrequency (%)
T558
100.0%
Other Punctuation
ValueCountFrequency (%)
:1116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10044
94.7%
Latin558
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
02193
21.8%
21909
19.0%
11155
11.5%
-1116
11.1%
:1116
11.1%
3649
 
6.5%
4398
 
4.0%
5395
 
3.9%
8342
 
3.4%
9287
 
2.9%
Other values (2)484
 
4.8%
Latin
ValueCountFrequency (%)
T558
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02193
20.7%
21909
18.0%
11155
10.9%
-1116
10.5%
:1116
10.5%
3649
 
6.1%
T558
 
5.3%
4398
 
3.8%
5395
 
3.7%
8342
 
3.2%
Other values (3)771
 
7.3%

periodoBalanco
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)2.6%
Missing4240
Missing (%)47.3%
Memory size70.2 KiB
2019-12-31T03:00:00
1729 
2019-12-31T00:00:00
1278 
2018-12-31T02:00:00
200 
2020-06-30T03:00:00
 
165
2018-12-31T00:00:00
 
141
Other values (119)
1220 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters89927
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.6%

Sample

1st row2019-09-30T00:00:00
2nd row2019-09-30T03:00:00
3rd row2018-12-31T02:00:00
4th row2019-06-30T03:00:00
5th row2018-12-31T02:00:00

Common Values

ValueCountFrequency (%)
2019-12-31T03:00:001729
19.3%
2019-12-31T00:00:001278
 
14.2%
2018-12-31T02:00:00200
 
2.2%
2020-06-30T03:00:00165
 
1.8%
2018-12-31T00:00:00141
 
1.6%
2020-07-31T03:00:0085
 
0.9%
2020-09-30T03:00:0085
 
0.9%
2019-12-31T06:00:0080
 
0.9%
2019-09-30T03:00:0073
 
0.8%
2020-12-31T03:00:0050
 
0.6%
Other values (114)847
 
9.4%
(Missing)4240
47.3%

Length

2021-08-21T09:45:00.914488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-12-31t03:00:001729
36.5%
2019-12-31t00:00:001278
27.0%
2018-12-31t02:00:00200
 
4.2%
2020-06-30t03:00:00165
 
3.5%
2018-12-31t00:00:00141
 
3.0%
2020-09-30t03:00:0085
 
1.8%
2020-07-31t03:00:0085
 
1.8%
2019-12-31t06:00:0080
 
1.7%
2019-09-30t03:00:0073
 
1.5%
2020-12-31t03:00:0050
 
1.1%
Other values (114)847
17.9%

Most occurring characters

ValueCountFrequency (%)
032565
36.2%
111948
 
13.3%
29561
 
10.6%
-9466
 
10.5%
:9466
 
10.5%
37368
 
8.2%
T4733
 
5.3%
93730
 
4.1%
8444
 
0.5%
6401
 
0.4%
Other values (3)245
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number66262
73.7%
Dash Punctuation9466
 
10.5%
Other Punctuation9466
 
10.5%
Uppercase Letter4733
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032565
49.1%
111948
 
18.0%
29561
 
14.4%
37368
 
11.1%
93730
 
5.6%
8444
 
0.7%
6401
 
0.6%
7157
 
0.2%
550
 
0.1%
438
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-9466
100.0%
Uppercase Letter
ValueCountFrequency (%)
T4733
100.0%
Other Punctuation
ValueCountFrequency (%)
:9466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common85194
94.7%
Latin4733
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
032565
38.2%
111948
 
14.0%
29561
 
11.2%
-9466
 
11.1%
:9466
 
11.1%
37368
 
8.6%
93730
 
4.4%
8444
 
0.5%
6401
 
0.5%
7157
 
0.2%
Other values (2)88
 
0.1%
Latin
ValueCountFrequency (%)
T4733
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII89927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032565
36.2%
111948
 
13.3%
29561
 
10.6%
-9466
 
10.5%
:9466
 
10.5%
37368
 
8.2%
T4733
 
5.3%
93730
 
4.1%
8444
 
0.5%
6401
 
0.4%
Other values (3)245
 
0.3%

ativoCirculante
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1794
Distinct (%)37.9%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean44510281.22
Minimum-17
Maximum2.903832 × 1010
Zeros554
Zeros (%)6.2%
Negative1
Negative (%)< 0.1%
Memory size70.2 KiB
2021-08-21T09:45:01.019489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile0
Q1887585
median3996630
Q316351166
95-th percentile134123658
Maximum2.903832 × 1010
Range2.903832002 × 1010
Interquartile range (IQR)15463581

Descriptive statistics

Standard deviation467453419.1
Coefficient of variation (CV)10.50214482
Kurtosis3154.765615
Mean44510281.22
Median Absolute Deviation (MAD)3989567
Skewness52.13686631
Sum2.10667161 × 1011
Variance2.185126991 × 1017
MonotonicityNot monotonic
2021-08-21T09:45:01.137489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0554
 
6.2%
4014767923
 
0.3%
21067790817
 
0.2%
2165299516
 
0.2%
30790555914
 
0.2%
276007414
 
0.2%
1376966014
 
0.2%
2039688312
 
0.1%
4253212312
 
0.1%
29732712
 
0.1%
Other values (1784)4045
45.1%
(Missing)4240
47.3%
ValueCountFrequency (%)
-171
 
< 0.1%
0554
6.2%
12
 
< 0.1%
21971
 
< 0.1%
70634
 
< 0.1%
106362
 
< 0.1%
145512
 
< 0.1%
148831
 
< 0.1%
195841
 
< 0.1%
239721
 
< 0.1%
ValueCountFrequency (%)
2.903832 × 10101
 
< 0.1%
86895130001
 
< 0.1%
24900383282
 
< 0.1%
20617940004
< 0.1%
20617840007
0.1%
14369850001
 
< 0.1%
13347260007
0.1%
12018580008
0.1%
11188860003
 
< 0.1%
9598550001
 
< 0.1%

passivoCirculante
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1789
Distinct (%)37.8%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean33968150.17
Minimum-1134941
Maximum2.750382 × 1010
Zeros591
Zeros (%)6.6%
Negative2
Negative (%)< 0.1%
Memory size70.2 KiB
2021-08-21T09:45:01.255525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1134941
5-th percentile0
Q1182970
median1335189
Q37449366
95-th percentile86495339
Maximum2.750382 × 1010
Range2.750495494 × 1010
Interquartile range (IQR)7266396

Descriptive statistics

Standard deviation494607401.7
Coefficient of variation (CV)14.5609166
Kurtosis2213.922895
Mean33968150.17
Median Absolute Deviation (MAD)1335189
Skewness44.07491523
Sum1.607712548 × 1011
Variance2.446364818 × 1017
MonotonicityNot monotonic
2021-08-21T09:45:01.366489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0591
 
6.6%
4022259123
 
0.3%
11967191517
 
0.2%
889656316
 
0.2%
74756214
 
0.2%
618107213
 
0.1%
8873012
 
0.1%
331680912
 
0.1%
115586612
 
0.1%
111127311
 
0.1%
Other values (1779)4012
44.7%
(Missing)4240
47.3%
ValueCountFrequency (%)
-11349411
 
< 0.1%
-3555091
 
< 0.1%
0591
6.6%
11
 
< 0.1%
2091
 
< 0.1%
2704
 
< 0.1%
4852
 
< 0.1%
10003
 
< 0.1%
10043
 
< 0.1%
13501
 
< 0.1%
ValueCountFrequency (%)
2.750382 × 10101
 
< 0.1%
1.305405467 × 10102
 
< 0.1%
27813330001
 
< 0.1%
163486200011
0.1%
10304950007
0.1%
9121040008
0.1%
6777960001
 
< 0.1%
5473809804
 
< 0.1%
5350658534
 
< 0.1%
5233600003
 
< 0.1%

totalAtivo
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1788
Distinct (%)37.8%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean70736227.55
Minimum-17
Maximum5.48235 × 1010
Zeros557
Zeros (%)6.2%
Negative1
Negative (%)< 0.1%
Memory size70.2 KiB
2021-08-21T09:45:01.485523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile0
Q11049740
median4637565
Q319167442
95-th percentile190511817
Maximum5.48235 × 1010
Range5.482350002 × 1010
Interquartile range (IQR)18117702

Descriptive statistics

Standard deviation887889128.9
Coefficient of variation (CV)12.55211311
Kurtosis3095.689797
Mean70736227.55
Median Absolute Deviation (MAD)4630502
Skewness51.80559965
Sum3.34794565 × 1011
Variance7.883471053 × 1017
MonotonicityNot monotonic
2021-08-21T09:45:01.596530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0557
 
6.2%
5602827723
 
0.3%
23185118817
 
0.2%
3549093815
 
0.2%
312178414
 
0.2%
1487809714
 
0.2%
51473912
 
0.1%
4853549512
 
0.1%
193737212
 
0.1%
2617129811
 
0.1%
Other values (1778)4046
45.1%
(Missing)4240
47.3%
ValueCountFrequency (%)
-171
 
< 0.1%
0557
6.2%
11
 
< 0.1%
70634
 
< 0.1%
106362
 
< 0.1%
121271
 
< 0.1%
148831
 
< 0.1%
185372
 
< 0.1%
244711
 
< 0.1%
300001
 
< 0.1%
ValueCountFrequency (%)
5.48235 × 10101
 
< 0.1%
1.8083486 × 10101
 
< 0.1%
90146924031
 
< 0.1%
36982159802
 
< 0.1%
363450600011
0.1%
26073510001
 
< 0.1%
21763690001
 
< 0.1%
20764230008
0.1%
17760120007
0.1%
17687420004
 
< 0.1%

totalPatrimonioLiquido
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1759
Distinct (%)37.2%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean28311717.52
Minimum-186719734
Maximum1.292328 × 1010
Zeros590
Zeros (%)6.6%
Negative196
Negative (%)2.2%
Memory size70.2 KiB
2021-08-21T09:45:01.710487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-186719734
5-th percentile0
Q1232892
median1569857
Q38036921
95-th percentile71634000
Maximum1.292328 × 1010
Range1.310999973 × 1010
Interquartile range (IQR)7804029

Descriptive statistics

Standard deviation257675516.1
Coefficient of variation (CV)9.101373518
Kurtosis1643.752994
Mean28311717.52
Median Absolute Deviation (MAD)1569857
Skewness36.46554306
Sum1.33999359 × 1011
Variance6.639667159 × 1016
MonotonicityNot monotonic
2021-08-21T09:45:01.842489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0590
 
6.6%
1513628223
 
0.3%
2320726115
 
0.2%
33101058114
 
0.2%
740237314
 
0.2%
237422114
 
0.2%
42014612
 
0.1%
24436412
 
0.1%
632278312
 
0.1%
2493412312
 
0.1%
Other values (1749)4015
44.7%
(Missing)4240
47.3%
ValueCountFrequency (%)
-1867197342
 
< 0.1%
-1120897802
 
< 0.1%
-1076266301
 
< 0.1%
-578798507
0.1%
-479615771
 
< 0.1%
-1951498210
0.1%
-133867202
 
< 0.1%
-106152041
 
< 0.1%
-62130002
 
< 0.1%
-37290081
 
< 0.1%
ValueCountFrequency (%)
1.292328 × 10101
 
< 0.1%
90141741051
 
< 0.1%
26309994122
 
< 0.1%
16132010003
 
< 0.1%
11164930004
 
< 0.1%
11120784443
 
< 0.1%
10036280001
 
< 0.1%
99005900011
0.1%
9106160002
 
< 0.1%
8639170003
 
< 0.1%

endividamento
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1185
Distinct (%)25.0%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean4687958.015
Minimum0
Maximum740631476
Zeros2381
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:01.965487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3741650
95-th percentile11300000
Maximum740631476
Range740631476
Interquartile range (IQR)741650

Descriptive statistics

Standard deviation37737155.59
Coefficient of variation (CV)8.049806647
Kurtosis261.2715524
Mean4687958.015
Median Absolute Deviation (MAD)0
Skewness15.48573803
Sum2.218810528 × 1010
Variance1.424092912 × 1015
MonotonicityNot monotonic
2021-08-21T09:45:02.088526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02381
26.5%
1577586218
 
0.2%
387760114
 
0.2%
113489012
 
0.1%
100000011
 
0.1%
460401111
 
0.1%
760332410
 
0.1%
21596809
 
0.1%
23134419
 
0.1%
19172608
 
0.1%
Other values (1175)2250
25.1%
(Missing)4240
47.3%
ValueCountFrequency (%)
02381
26.5%
11
 
< 0.1%
10003
 
< 0.1%
10361
 
< 0.1%
15002
 
< 0.1%
17802
 
< 0.1%
19752
 
< 0.1%
19791
 
< 0.1%
23481
 
< 0.1%
31602
 
< 0.1%
ValueCountFrequency (%)
7406314761
 
< 0.1%
7210960001
 
< 0.1%
6885000003
< 0.1%
6492140004
< 0.1%
6480000001
 
< 0.1%
6115660003
< 0.1%
3265580003
< 0.1%
2751270002
< 0.1%
1860000001
 
< 0.1%
1850000001
 
< 0.1%

duplicatasAReceber
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1668
Distinct (%)35.2%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean16633967.48
Minimum-22780710
Maximum2.009358 × 1010
Zeros1029
Zeros (%)11.5%
Negative4
Negative (%)< 0.1%
Memory size70.2 KiB
2021-08-21T09:45:02.210490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-22780710
5-th percentile0
Q139205
median1088164
Q36576243
95-th percentile45010042
Maximum2.009358 × 1010
Range2.011636071 × 1010
Interquartile range (IQR)6537038

Descriptive statistics

Standard deviation297902910.3
Coefficient of variation (CV)17.90931181
Kurtosis4363.202731
Mean16633967.48
Median Absolute Deviation (MAD)1088164
Skewness64.88105035
Sum7.87285681 × 1010
Variance8.874614395 × 1016
MonotonicityNot monotonic
2021-08-21T09:45:02.340523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01029
 
11.5%
1479913723
 
0.3%
13533030917
 
0.2%
923955616
 
0.2%
4356511214
 
0.2%
673307712
 
0.1%
398420812
 
0.1%
1726745212
 
0.1%
564185111
 
0.1%
40079900011
 
0.1%
Other values (1658)3576
39.9%
(Missing)4240
47.3%
ValueCountFrequency (%)
-227807102
 
< 0.1%
-13134162
 
< 0.1%
01029
11.5%
11
 
< 0.1%
1462
 
< 0.1%
9241
 
< 0.1%
11431
 
< 0.1%
11782
 
< 0.1%
16131
 
< 0.1%
16532
 
< 0.1%
ValueCountFrequency (%)
2.009358 × 10101
 
< 0.1%
22394550001
 
< 0.1%
19697100001
 
< 0.1%
5393931752
 
< 0.1%
5285420003
 
< 0.1%
4829900003
 
< 0.1%
4761720001
 
< 0.1%
40079900011
0.1%
3416680001
 
< 0.1%
2757710002
 
< 0.1%

estoque
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1674
Distinct (%)35.4%
Missing4240
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean15239556.06
Minimum-263226
Maximum1293428000
Zeros763
Zeros (%)8.5%
Negative3
Negative (%)< 0.1%
Memory size70.2 KiB
2021-08-21T09:45:02.460549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-263226
5-th percentile0
Q1171286
median1063783
Q35493839
95-th percentile44444561
Maximum1293428000
Range1293691226
Interquartile range (IQR)5322553

Descriptive statistics

Standard deviation83837924.13
Coefficient of variation (CV)5.501336377
Kurtosis144.8658006
Mean15239556.06
Median Absolute Deviation (MAD)1063783
Skewness11.16456174
Sum7.212881883 × 1010
Variance7.028797522 × 1015
MonotonicityNot monotonic
2021-08-21T09:45:02.584493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0763
 
8.5%
2366458323
 
0.3%
4444456117
 
0.2%
924065716
 
0.2%
4599065114
 
0.2%
120695514
 
0.2%
407611014
 
0.2%
1530537312
 
0.1%
92345212
 
0.1%
645015512
 
0.1%
Other values (1664)3836
42.8%
(Missing)4240
47.3%
ValueCountFrequency (%)
-2632261
 
< 0.1%
-1480981
 
< 0.1%
-1480951
 
< 0.1%
0763
8.5%
11
 
< 0.1%
2911
 
< 0.1%
3902
 
< 0.1%
9422
 
< 0.1%
12501
 
< 0.1%
26532
 
< 0.1%
ValueCountFrequency (%)
129342800011
0.1%
7664660008
0.1%
7005410007
0.1%
5546719512
 
< 0.1%
5150030001
 
< 0.1%
5124730001
 
< 0.1%
5062972024
 
< 0.1%
4688350001
 
< 0.1%
4668350001
 
< 0.1%
4269864787
0.1%

faturamentoBruto
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct4288
Distinct (%)52.1%
Missing750
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean55974202.68
Minimum0
Maximum6426115000
Zeros414
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:02.716490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11191995
median3599483
Q315842218
95-th percentile160466853.4
Maximum6426115000
Range6426115000
Interquartile range (IQR)14650223

Descriptive statistics

Standard deviation334435671.7
Coefficient of variation (CV)5.974817964
Kurtosis208.505547
Mean55974202.68
Median Absolute Deviation (MAD)3149529
Skewness13.08225231
Sum4.602758686 × 1011
Variance1.118472185 × 1017
MonotonicityNot monotonic
2021-08-21T09:45:02.846488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0414
 
4.6%
10000018
 
0.2%
12077617516
 
0.2%
4427694016
 
0.2%
64215114915
 
0.2%
785149914
 
0.2%
2289815412
 
0.1%
1212369912
 
0.1%
4350997912
 
0.1%
11683079912
 
0.1%
Other values (4278)7682
85.6%
(Missing)750
 
8.4%
ValueCountFrequency (%)
0414
4.6%
13
 
< 0.1%
10001
 
< 0.1%
11232
 
< 0.1%
50143
 
< 0.1%
110754
 
< 0.1%
114341
 
< 0.1%
119462
 
< 0.1%
125002
 
< 0.1%
126001
 
< 0.1%
ValueCountFrequency (%)
642611500011
0.1%
49459260001
 
< 0.1%
44640000001
 
< 0.1%
35085520001
 
< 0.1%
33691730008
0.1%
32224020006
0.1%
29263012014
 
< 0.1%
27700990001
 
< 0.1%
27116010001
 
< 0.1%
24497656001
 
< 0.1%

margemBruta
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1929
Distinct (%)23.5%
Missing750
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean16209878.9
Minimum-614872100
Maximum3366842514
Zeros4316
Zeros (%)48.1%
Negative68
Negative (%)0.8%
Memory size70.2 KiB
2021-08-21T09:45:02.973514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-614872100
5-th percentile0
Q10
median0
Q33357474.5
95-th percentile38268767.2
Maximum3366842514
Range3981714614
Interquartile range (IQR)3357474.5

Descriptive statistics

Standard deviation116348171.3
Coefficient of variation (CV)7.177608912
Kurtosis306.8502976
Mean16209878.9
Median Absolute Deviation (MAD)0
Skewness15.36587893
Sum1.332938342 × 1011
Variance1.353689695 × 1016
MonotonicityNot monotonic
2021-08-21T09:45:03.101488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04316
48.1%
3354589116
 
0.2%
1955028416
 
0.2%
296788114
 
0.2%
21527337513
 
0.1%
563694512
 
0.1%
332352312
 
0.1%
1821324612
 
0.1%
203257900011
 
0.1%
28810021011
 
0.1%
Other values (1919)3790
42.2%
(Missing)750
 
8.4%
ValueCountFrequency (%)
-6148721002
 
< 0.1%
-2693832071
 
< 0.1%
-267785291
 
< 0.1%
-216846281
 
< 0.1%
-102153041
 
< 0.1%
-75405441
 
< 0.1%
-44262532
 
< 0.1%
-41118222
 
< 0.1%
-41118211
 
< 0.1%
-28461718
0.1%
ValueCountFrequency (%)
33668425142
 
< 0.1%
203257900011
0.1%
13731430822
 
< 0.1%
10635288884
 
< 0.1%
10390540001
 
< 0.1%
10390000001
 
< 0.1%
10386870004
 
< 0.1%
9981740004
 
< 0.1%
9684480003
 
< 0.1%
8377108793
 
< 0.1%

periodoDemonstrativoEmMeses
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.1%
Missing750
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean10.3773562
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:03.212489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median12
Q312
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.220964525
Coefficient of variation (CV)0.3103839228
Kurtosis2.175683337
Mean10.3773562
Median Absolute Deviation (MAD)0
Skewness-1.870275549
Sum85333
Variance10.37461247
MonotonicityNot monotonic
2021-08-21T09:45:03.292526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
126138
68.4%
1460
 
5.1%
6448
 
5.0%
7205
 
2.3%
9190
 
2.1%
11158
 
1.8%
3141
 
1.6%
5137
 
1.5%
10123
 
1.4%
8115
 
1.3%
Other values (2)108
 
1.2%
(Missing)750
 
8.4%
ValueCountFrequency (%)
1460
5.1%
222
 
0.2%
3141
 
1.6%
486
 
1.0%
5137
 
1.5%
6448
5.0%
7205
2.3%
8115
 
1.3%
9190
2.1%
10123
 
1.4%
ValueCountFrequency (%)
126138
68.4%
11158
 
1.8%
10123
 
1.4%
9190
 
2.1%
8115
 
1.3%
7205
 
2.3%
6448
 
5.0%
5137
 
1.5%
486
 
1.0%
3141
 
1.6%

custos
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1743
Distinct (%)21.2%
Missing750
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean28390609.77
Minimum-346633805
Maximum4393536000
Zeros4429
Zeros (%)49.4%
Negative28
Negative (%)0.3%
Memory size70.2 KiB
2021-08-21T09:45:03.394524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-346633805
5-th percentile0
Q10
median0
Q34520907
95-th percentile79202115
Maximum4393536000
Range4740169805
Interquartile range (IQR)4520907

Descriptive statistics

Standard deviation207214754.5
Coefficient of variation (CV)7.29870743
Kurtosis288.318444
Mean28390609.77
Median Absolute Deviation (MAD)0
Skewness15.49362933
Sum2.334559842 × 1011
Variance4.293795449 × 1016
MonotonicityNot monotonic
2021-08-21T09:45:03.515489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04429
49.4%
8723028423
 
0.3%
2472665616
 
0.2%
42687777414
 
0.2%
488361814
 
0.2%
8578556113
 
0.1%
1726120912
 
0.1%
2529673312
 
0.1%
880017612
 
0.1%
439353600011
 
0.1%
Other values (1733)3667
40.9%
(Missing)750
 
8.4%
ValueCountFrequency (%)
-3466338053
< 0.1%
-650227015
0.1%
-492677381
 
< 0.1%
-488366471
 
< 0.1%
-433399922
 
< 0.1%
-275926681
 
< 0.1%
-257445971
 
< 0.1%
-94554011
 
< 0.1%
-72315541
 
< 0.1%
-70364741
 
< 0.1%
ValueCountFrequency (%)
439353600011
0.1%
29582350001
 
< 0.1%
23304860004
 
< 0.1%
23301730001
 
< 0.1%
23301190001
 
< 0.1%
19533080002
 
< 0.1%
19397880001
 
< 0.1%
19167620001
 
< 0.1%
18627723134
 
< 0.1%
16008770002
 
< 0.1%

anoFundacao
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct69
Distinct (%)0.8%
Missing745
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2006.027467
Minimum1000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:03.637526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1984
Q12000
median2009
Q32015
95-th percentile2019
Maximum2020
Range1020
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.42412
Coefficient of variation (CV)0.009682878382
Kurtosis1748.393394
Mean2006.027467
Median Absolute Deviation (MAD)7
Skewness-34.09741697
Sum16505594
Variance377.2964376
MonotonicityNot monotonic
2021-08-21T09:45:03.757513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017556
 
6.2%
2018477
 
5.3%
2016441
 
4.9%
2010397
 
4.4%
2013395
 
4.4%
2019374
 
4.2%
2011348
 
3.9%
2012341
 
3.8%
2009328
 
3.7%
2008327
 
3.6%
Other values (59)4244
47.3%
(Missing)745
 
8.3%
ValueCountFrequency (%)
10002
 
< 0.1%
19101
 
< 0.1%
19351
 
< 0.1%
19411
 
< 0.1%
19429
0.1%
19461
 
< 0.1%
19472
 
< 0.1%
19489
0.1%
19516
0.1%
19561
 
< 0.1%
ValueCountFrequency (%)
202053
 
0.6%
2019374
4.2%
2018477
5.3%
2017556
6.2%
2016441
4.9%
2015324
3.6%
2014316
3.5%
2013395
4.4%
2012341
3.8%
2011348
3.9%

intervaloFundacao
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing745
Missing (%)8.3%
Memory size70.2 KiB
Acima de 17 anos
2681 
De 0 a 5 anos
2199 
De 6 a 10 anos
1785 
De 11 a 16 anos
1563 

Length

Max length16
Median length15
Mean length14.57438017
Min length13

Characters and Unicode

Total characters119918
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAcima de 17 anos
2nd rowDe 6 a 10 anos
3rd rowDe 6 a 10 anos
4th rowAcima de 17 anos
5th rowDe 6 a 10 anos

Common Values

ValueCountFrequency (%)
Acima de 17 anos2681
29.9%
De 0 a 5 anos2199
24.5%
De 6 a 10 anos1785
19.9%
De 11 a 16 anos1563
17.4%
(Missing)745
 
8.3%

Length

2021-08-21T09:45:03.955488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T09:45:04.018490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
de8228
21.4%
anos8228
21.4%
a5547
14.4%
acima2681
 
7.0%
172681
 
7.0%
52199
 
5.7%
02199
 
5.7%
61785
 
4.6%
101785
 
4.6%
111563
 
4.1%

Most occurring characters

ValueCountFrequency (%)
30231
25.2%
a16456
13.7%
19155
 
7.6%
e8228
 
6.9%
n8228
 
6.9%
o8228
 
6.9%
s8228
 
6.9%
D5547
 
4.6%
03984
 
3.3%
63348
 
2.8%
Other values (7)18285
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60092
50.1%
Space Separator30231
25.2%
Decimal Number21367
 
17.8%
Uppercase Letter8228
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a16456
27.4%
e8228
13.7%
n8228
13.7%
o8228
13.7%
s8228
13.7%
c2681
 
4.5%
i2681
 
4.5%
m2681
 
4.5%
d2681
 
4.5%
Decimal Number
ValueCountFrequency (%)
19155
42.8%
03984
18.6%
63348
 
15.7%
72681
 
12.5%
52199
 
10.3%
Uppercase Letter
ValueCountFrequency (%)
D5547
67.4%
A2681
32.6%
Space Separator
ValueCountFrequency (%)
30231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68320
57.0%
Common51598
43.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a16456
24.1%
e8228
12.0%
n8228
12.0%
o8228
12.0%
s8228
12.0%
D5547
 
8.1%
A2681
 
3.9%
c2681
 
3.9%
i2681
 
3.9%
m2681
 
3.9%
Common
ValueCountFrequency (%)
30231
58.6%
19155
 
17.7%
03984
 
7.7%
63348
 
6.5%
72681
 
5.2%
52199
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII119918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30231
25.2%
a16456
13.7%
19155
 
7.6%
e8228
 
6.9%
n8228
 
6.9%
o8228
 
6.9%
s8228
 
6.9%
D5547
 
4.6%
03984
 
3.3%
63348
 
2.8%
Other values (7)18285
15.2%

capitalSocial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct403
Distinct (%)4.9%
Missing745
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean11214526.11
Minimum0
Maximum4100000000
Zeros129
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:04.110548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8675
Q150000
median100000
Q3500000
95-th percentile6000000
Maximum4100000000
Range4100000000
Interquartile range (IQR)450000

Descriptive statistics

Standard deviation97428533.71
Coefficient of variation (CV)8.68770849
Kurtosis494.7909869
Mean11214526.11
Median Absolute Deviation (MAD)90000
Skewness17.51507246
Sum9.227312084 × 1010
Variance9.492319181 × 1015
MonotonicityNot monotonic
2021-08-21T09:45:04.228492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000001134
 
12.6%
200000443
 
4.9%
10000416
 
4.6%
50000383
 
4.3%
20000307
 
3.4%
30000290
 
3.2%
500000288
 
3.2%
150000269
 
3.0%
1000000264
 
2.9%
300000241
 
2.7%
Other values (393)4193
46.7%
(Missing)745
 
8.3%
ValueCountFrequency (%)
0129
1.4%
115
 
0.2%
29
 
0.1%
1003
 
< 0.1%
2402
 
< 0.1%
3001
 
< 0.1%
100042
 
0.5%
13001
 
< 0.1%
15007
 
0.1%
19782
 
< 0.1%
ValueCountFrequency (%)
41000000001
 
< 0.1%
25000000001
 
< 0.1%
13422400007
 
0.1%
97995743227
0.3%
9120000001
 
< 0.1%
9000000003
 
< 0.1%
8345102661
 
< 0.1%
5767238581
 
< 0.1%
5620166502
 
< 0.1%
5119031869
 
0.1%

restricoes
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing745
Missing (%)8.3%
Memory size70.2 KiB
False
7458 
True
770 
(Missing)
 
745
ValueCountFrequency (%)
False7458
83.1%
True770
 
8.6%
(Missing)745
 
8.3%
2021-08-21T09:45:04.306515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

empresa_MeEppMei
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing745
Missing (%)8.3%
Memory size70.2 KiB
False
5046 
True
3182 
(Missing)
745 
ValueCountFrequency (%)
False5046
56.2%
True3182
35.5%
(Missing)745
 
8.3%
2021-08-21T09:45:04.343490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

scorePontualidade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct385
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.798345684
Minimum0
Maximum1
Zeros1556
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:04.428524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8874787362
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1125212638

Descriptive statistics

Standard deviation0.3791861768
Coefficient of variation (CV)0.4749648985
Kurtosis0.530184072
Mean0.798345684
Median Absolute Deviation (MAD)0
Skewness-1.553525613
Sum7163.555823
Variance0.1437821567
MonotonicityNot monotonic
2021-08-21T09:45:04.551491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15594
62.3%
01556
 
17.3%
0.830643615621
 
0.2%
0.998127419316
 
0.2%
0.994785850716
 
0.2%
0.986135484216
 
0.2%
0.989597025216
 
0.2%
0.991504780216
 
0.2%
0.996327069515
 
0.2%
0.986102886114
 
0.2%
Other values (375)1693
 
18.9%
ValueCountFrequency (%)
01556
17.3%
0.00300244716
 
0.1%
0.03869844383
 
< 0.1%
0.06837464656
 
0.1%
0.09551107581
 
< 0.1%
0.10286865422
 
< 0.1%
0.11298890021
 
< 0.1%
0.11457742484
 
< 0.1%
0.11895994921
 
< 0.1%
0.15655140521
 
< 0.1%
ValueCountFrequency (%)
15594
62.3%
0.99999997636
 
0.1%
0.99999986244
 
< 0.1%
0.9999987847
 
0.1%
0.999993071111
 
0.1%
0.99998189619
 
0.1%
0.99997585535
 
0.1%
0.99993039458
 
0.1%
0.99991758234
 
< 0.1%
0.99977729336
 
0.1%

limiteEmpresaAnaliseCredito
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1810
Distinct (%)22.0%
Missing745
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2851016.646
Minimum0
Maximum1974261312
Zeros528
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2021-08-21T09:45:04.674524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17360
median48600
Q3345000
95-th percentile8728464
Maximum1974261312
Range1974261312
Interquartile range (IQR)337640

Descriptive statistics

Standard deviation26723244.37
Coefficient of variation (CV)9.373233374
Kurtosis3619.608286
Mean2851016.646
Median Absolute Deviation (MAD)46680
Skewness51.13390401
Sum2.345816496 × 1010
Variance7.141317899 × 1014
MonotonicityNot monotonic
2021-08-21T09:45:04.801522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0528
 
5.9%
720154
 
1.7%
540085
 
0.9%
440079
 
0.9%
1628070
 
0.8%
880069
 
0.8%
1620063
 
0.7%
704061
 
0.7%
1080061
 
0.7%
264058
 
0.6%
Other values (1800)7000
78.0%
(Missing)745
 
8.3%
ValueCountFrequency (%)
0528
5.9%
720154
 
1.7%
8101
 
< 0.1%
8404
 
< 0.1%
8804
 
< 0.1%
9607
 
0.1%
10003
 
< 0.1%
10802
 
< 0.1%
11001
 
< 0.1%
111017
 
0.2%
ValueCountFrequency (%)
19742613121
 
< 0.1%
2867581449
 
0.1%
2483129601
 
< 0.1%
2436173601
 
< 0.1%
2305231202
 
< 0.1%
1399151523
 
< 0.1%
1381593601
 
< 0.1%
13223315228
0.3%
1284306842
 
< 0.1%
1228083201
 
< 0.1%

dataAprovadoNivelAnalista
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct7011
Distinct (%)100.0%
Missing1962
Missing (%)21.9%
Memory size70.2 KiB
2020-02-07T21:11:17
 
1
2020-05-08T18:10:10
 
1
2020-03-02T21:22:20
 
1
2020-10-30T17:16:20
 
1
2020-02-13T17:29:27
 
1
Other values (7006)
7006 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters133209
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7011 ?
Unique (%)100.0%

Sample

1st row2020-02-03T20:57:33
2nd row2020-02-04T16:40:49
3rd row2020-02-04T16:37:52
4th row2020-02-04T15:06:28
5th row2020-02-04T15:10:46

Common Values

ValueCountFrequency (%)
2020-02-07T21:11:171
 
< 0.1%
2020-05-08T18:10:101
 
< 0.1%
2020-03-02T21:22:201
 
< 0.1%
2020-10-30T17:16:201
 
< 0.1%
2020-02-13T17:29:271
 
< 0.1%
2020-07-23T17:55:081
 
< 0.1%
2020-11-24T20:03:451
 
< 0.1%
2020-09-02T13:08:031
 
< 0.1%
2020-09-10T15:00:101
 
< 0.1%
2020-10-23T13:41:561
 
< 0.1%
Other values (7001)7001
78.0%
(Missing)1962
 
21.9%

Length

2021-08-21T09:45:05.046511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-23t19:46:281
 
< 0.1%
2020-05-28t15:59:341
 
< 0.1%
2021-01-27t15:19:311
 
< 0.1%
2020-12-02t14:18:471
 
< 0.1%
2020-12-07t13:43:261
 
< 0.1%
2020-11-24t20:32:071
 
< 0.1%
2020-12-07t19:38:401
 
< 0.1%
2020-06-25t12:27:121
 
< 0.1%
2020-05-27t15:45:591
 
< 0.1%
2020-09-04t14:57:161
 
< 0.1%
Other values (7001)7001
99.9%

Most occurring characters

ValueCountFrequency (%)
025936
19.5%
224261
18.2%
117113
12.8%
-14022
10.5%
:14022
10.5%
T7011
 
5.3%
35911
 
4.4%
55624
 
4.2%
45382
 
4.0%
93800
 
2.9%
Other values (3)10127
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number98154
73.7%
Dash Punctuation14022
 
10.5%
Other Punctuation14022
 
10.5%
Uppercase Letter7011
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025936
26.4%
224261
24.7%
117113
17.4%
35911
 
6.0%
55624
 
5.7%
45382
 
5.5%
93800
 
3.9%
83667
 
3.7%
73434
 
3.5%
63026
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-14022
100.0%
Uppercase Letter
ValueCountFrequency (%)
T7011
100.0%
Other Punctuation
ValueCountFrequency (%)
:14022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common126198
94.7%
Latin7011
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
025936
20.6%
224261
19.2%
117113
13.6%
-14022
11.1%
:14022
11.1%
35911
 
4.7%
55624
 
4.5%
45382
 
4.3%
93800
 
3.0%
83667
 
2.9%
Other values (2)6460
 
5.1%
Latin
ValueCountFrequency (%)
T7011
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII133209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025936
19.5%
224261
18.2%
117113
12.8%
-14022
10.5%
:14022
10.5%
T7011
 
5.3%
35911
 
4.4%
55624
 
4.2%
45382
 
4.0%
93800
 
2.9%
Other values (3)10127
 
7.6%

Interactions

2021-08-21T09:43:41.292223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:43:41.396224image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-08-21T09:44:45.063445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.162444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.270483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.362453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.459480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.554444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.666479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.764481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.862447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:45.960445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.062446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.162448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.262445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.356444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.451480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.543447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.635444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.733445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.830445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:46.934481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.030482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.122448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.222446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.321443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.409480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.502445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.596446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.705477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.809445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:47.903481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.003478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.101478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.205445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.346447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.451482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.558448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.667469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.770445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.875447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:48.976478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.083479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.180482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.274482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.373479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.472479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.575480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.675479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.788487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:49.898483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.003473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.108445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.213471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.341443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.439480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.545445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.653448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.766444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.879447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:50.988445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.107481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.222448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.335445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.453444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.577478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.691481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.798444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:51.908479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.016479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.120445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.230446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.341481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.455444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.566448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.675481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.789481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:52.899486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:53.004481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:53.113446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:53.219479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T09:44:53.327480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-08-21T09:45:05.165523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-21T09:45:05.458491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-21T09:45:05.746524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-21T09:45:06.053489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-21T09:45:06.330523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-21T09:44:54.050481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-21T09:44:55.143483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-21T09:44:55.661479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-21T09:44:56.180480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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